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Bat detective - Deep learning tools for bat acoustic signal detection

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Title: Bat detective - Deep learning tools for bat acoustic signal detection
Authors: Mac Aodha, O
Gibb, R
Barlow, KE
Browning, E
Firman, M
Freeman, R
Harder, B
Kinsey, L
Mead, GR
Newson, SE
Pandourski, I
Parsons, S
Russ, J
Szodoray-Paradi, A
Szodoray-Paradi, F
Tilova, E
Girolami, M
Brostow, G
Jones, KE
Item Type: Journal Article
Abstract: Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.
Issue Date: 8-Mar-2018
Date of Acceptance: 21-Jan-2018
URI: http://hdl.handle.net/10044/1/66131
DOI: https://dx.doi.org/10.1371/journal.pcbi.1005995
ISSN: 1553-734X
Publisher: Public Library of Science (PLoS)
Journal / Book Title: PLoS Computational Biology
Volume: 14
Issue: 3
Copyright Statement: © 2018 Mac Aodha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
ARTIFICIAL NEURAL-NETWORKS
ECHOLOCATION CALLS
CITIZEN SCIENCE
AUTOMATED IDENTIFICATION
MONITORING PROGRAM
SPEECH RECOGNITION
CLASSIFICATION
BIODIVERSITY
INFORMATION
POPULATIONS
Algorithms
Animals
Chiroptera
Computational Biology
Echolocation
Endangered Species
Environmental Monitoring
Machine Learning
Neural Networks (Computer)
Signal Processing, Computer-Assisted
Zoology
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Bioinformatics
Publication Status: Published
Article Number: e1005995
Online Publication Date: 2018-03-08
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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